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Survival of Persons with Down Syndrome in Italy

2014· article· en· W2079322598 on OpenAlexvenueno aff
Aldo Rosano

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2014
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
FundersFondazione Cariplo
KeywordsLife expectancyDemographyMedicineDisadvantageSurvival analysisPediatricsGerontologyPopulationSurgery

Abstract

fetched live from OpenAlex

Down syndrome (DS) is a major cause of congenital malformation and disability. No updated data are available on life expectancy of persons with DS in Europe. We collected information on age, sex and area of birth of 3,217 persons with DS died from 1997 to 2009 in Italy. Survivals rates and mean survival time was calculated using a life tables calculated from cross sectional data. Some factors influencing the survival were also analysed using a semi-proportional hazard model. Survival rates of 91.4% at one year and 88.3% at ten years were found. Mean survival time at birth was 47.1 years (C.I. 95%: 46.5-47.7). There was 8-year significant difference in survival between north-central regions and southern regions. Male life expectancy was 46.9 years (C.I. 95%: 46.1-47.8), lower than females 47.3 years (C.I. 95%: 46.5-48.2) even though not statistically significant (p=0.23). Almost nine out ten children with DS now survive at least 10 years. Adequate educational and health service provisions needs to be made for them. The disadvantage of Down persons born in the Southern regions in terms of life expectancy is impressive. Quality of medical care provided in the South of Italy in the first months of life is the most likely determinant of the high mortality observed among persons with DS born in that area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.297
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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